PB1992: HISPANIC ETHNICITY AS A STRONG PREDICTOR OF POOR RESPONSE TO INDUCTION THERAPY AND ASCT IN TRANSPLANT ELIGIBLE MULTIPLE MYELOMA PATIENTS
Authors: I. Chineke, E. Lu, H. Win, A. Larsen, G. Yao, F. Chilton, K. Gowin
Journal: HemaSphere (2022)

Background: Attempts have been made to use machine learning analysis of multidimensional data to determine personalized therapeutic approaches in multiple myeloma. Outcomes of disparities in Hispanic multiple myeloma treatment have been attributed in part to inadequate access to novel therapies and autologous stem cell transplant (ASCT). We sought to utilize a machine learning approach to identify clinical and demographic features associated with patient responses to multiple myeloma induction therapy and ASCT Aims: We aimed to employ machine learning analysis in the identification of clinical and demographic characteristics associated with patients’ response to multiple myeloma induction therapy and ASCT Methods: A retrospective chart review of patients with a documented diagnosis of multiple myeloma seen at the University of Arizona Cancer Center between January 2017 and April 2021 was performed. RedCapp was utilized to organize demographic, clinical, laboratory, therapeutic, and outcome data. A patient’s best response (per the IMWG myeloma response criteria) following induction therapy prior to ASCT was classified into the following categories: progression of disease, less than PR (partial response), partial response (PR), very good partial response (VGPR), complete response (CR), and stringent complete response (sCR). This response was then compared to the response at 100 days post-transplant and labeled as ‘1’ if the response improved or ‘0’ if the response stayed the same or worsened. A clinical decision tree model was generated by recursive partitioning and minimizing cross-validation error, which outperformed other tested models (e.g elastic net and LASSO regression) in accuracy. Critical indices that best stratified patient’s response (1 vs 0) were then identified based on the tree model. Results: Of 112 patients reviewed, 48 were transplant-eligible and had complete data for analysis. Among available input features derived from RedCapp, (including demographic and clinical baseline factors and the choice of first induction regimen), ethnicity (Hispanic/non-Hispanic) was identified as the central node in the tree model that stratified patient responses. Among Hispanic patients (23% in this cohort), only 18% showed any improvement in their documented best response when comparing induction therapy and 100 days after transplant. In contrast, among non-Hispanic patients (77% in this cohort), 68% exhibited an improvement. Furthermore, serum creatinine (sCr) was identified as the key risk factor in non-Hispanic patients, with those having sCr ≥ 1.6 (mg/dL) showing a much smaller chance of improvement (17%) on day 100 post-transplant compared with patients with sCr < 1.6 (79%). Image:Summary/Conclusion: Utilizing a machine learning-enabled analysis of multidimensional data from 48 patients, we identified Hispanic ethnicity and renal dysfunction at the time of diagnosis (sCr ≥ 1.6) as key factors that may impact patient responses between the end of induction therapy and at 100 days post-transplant. Although limited to a single institution and a small sample size, these findings warrant further evaluation in larger patient cohorts, and raise important questions regarding the pathobiology and corresponding therapeutic strategies for multiple myeloma in Hispanic populations. This study further illustrates the utility of data-driven approaches such as machine learning to shed light on complex issues such as cancer care and related disparities as well as the pathobiology and therapy choice of the complex disease of multiple myeloma.